{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91ad9bfe",
   "metadata": {},
   "source": [
    "[AlexNet Paper](./NIPS-2012-imagenet-classification-with-deep-convolutional-neural-networks-Paper.pdf)\n",
    "\n",
    "![image-20250622110954912](http://assets.hypervoid.top/img/2025/06/22/image-20250622110954912-74c6.png)\n",
    "\n",
    "![image-20250622162539576](http://assets.hypervoid.top/img/2025/06/22/image-20250622162539576-46bc.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41a3e2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3af5e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.autograd.anomaly_mode.set_detect_anomaly at 0x2808f7cb290>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available else 'cpu')\n",
    "batch_size = 192\n",
    "num_epoch = 50\n",
    "num_class = 10 # cifar10\n",
    "lr = 1e-3\n",
    "loss_func = nn.CrossEntropyLoss()\n",
    "torch.autograd.set_detect_anomaly(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cefae091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "torch.Size([3, 227, 227]) 6\n"
     ]
    }
   ],
   "source": [
    "import torchvision as tv\n",
    "from torchvision import transforms\n",
    "\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((227,227)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "train_data = tv.datasets.cifar.CIFAR10(\"./cifar10_dataset\",train=True,  download=True, transform=transform)\n",
    "test_data = tv.datasets.cifar.CIFAR10(\"./cifar10_dataset\",train=False,  download=True, transform=transform)\n",
    "\n",
    "train_iter = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "test_iter = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "for x, y in train_data:\n",
    "    print(x.shape, y)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bb95260",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import Tensor\n",
    "\n",
    "\n",
    "class AlexNet(nn.Module):\n",
    "    def __init__(self, num_feats=1000):\n",
    "        super(AlexNet, self).__init__()\n",
    "        self.num_feats = num_feats\n",
    "        self.conv1 = nn.Sequential(\n",
    "            # 输入: (N, 3, 227, 227)\n",
    "            nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4),\n",
    "            # 输出 （N, 96, 55, 55）\n",
    "            nn.BatchNorm2d(),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),  # (N, 96, 27, 27)\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            # 输入: (N, 96, 27, 27)\n",
    "            nn.Conv2d(in_channels=96, out_channels=256, kernel_size=5, padding=2),\n",
    "            # 输出 (N, 256, 13, 13)\n",
    "            nn.BatchNorm2d(),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),  # (N, 256, 13, 13)\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(\n",
    "            # 输入: (N, 256, 13, 13)\n",
    "            nn.Conv2d(in_channels=256, out_channels=384, kernel_size=3, padding='same'),\n",
    "            nn.BatchNorm2d(),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv2d(384, 384, kernel_size=3, padding='same'),\n",
    "            nn.BatchNorm2d(),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.conv5 = nn.Sequential(\n",
    "            nn.Conv2d(384, 256, kernel_size=3, padding='same'),\n",
    "            nn.BatchNorm2d(),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            # 输出 (N, 256, 6, 6)\n",
    "        )\n",
    "        self.encoder = nn.Sequential(\n",
    "            self.conv1,\n",
    "            self.conv2,\n",
    "            self.conv3,\n",
    "            self.conv4,\n",
    "            self.conv5,\n",
    "        )\n",
    "        # 输出 (N, 256, 6, 6)\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Dropout(p=0.5, ),\n",
    "            nn.Linear(in_features=(256 * 6 * 6), out_features=4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5, ),\n",
    "            nn.Linear(in_features=4096, out_features=4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=4096, out_features=self.num_feats),\n",
    "        )\n",
    "    def forward(self, x:Tensor):\n",
    "        enc = self.encoder(x)\n",
    "        return self.classifier(enc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ecfdb8e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n",
      "Layer (type:depth-idx)                   Param #\n",
      "=================================================================\n",
      "├─Sequential: 1-1                        --\n",
      "|    └─Conv2d: 2-1                       34,944\n",
      "|    └─ReLU: 2-2                         --\n",
      "|    └─MaxPool2d: 2-3                    --\n",
      "├─Sequential: 1-2                        --\n",
      "|    └─Conv2d: 2-4                       614,656\n",
      "|    └─ReLU: 2-5                         --\n",
      "|    └─MaxPool2d: 2-6                    --\n",
      "├─Sequential: 1-3                        --\n",
      "|    └─Conv2d: 2-7                       885,120\n",
      "|    └─ReLU: 2-8                         --\n",
      "├─Sequential: 1-4                        --\n",
      "|    └─Conv2d: 2-9                       1,327,488\n",
      "|    └─ReLU: 2-10                        --\n",
      "├─Sequential: 1-5                        --\n",
      "|    └─Conv2d: 2-11                      884,992\n",
      "|    └─ReLU: 2-12                        --\n",
      "|    └─MaxPool2d: 2-13                   --\n",
      "├─Sequential: 1-6                        --\n",
      "|    └─Sequential: 2-14                  (recursive)\n",
      "|    |    └─Conv2d: 3-1                  (recursive)\n",
      "|    |    └─ReLU: 3-2                    --\n",
      "|    |    └─MaxPool2d: 3-3               --\n",
      "|    └─Sequential: 2-15                  (recursive)\n",
      "|    |    └─Conv2d: 3-4                  (recursive)\n",
      "|    |    └─ReLU: 3-5                    --\n",
      "|    |    └─MaxPool2d: 3-6               --\n",
      "|    └─Sequential: 2-16                  (recursive)\n",
      "|    |    └─Conv2d: 3-7                  (recursive)\n",
      "|    |    └─ReLU: 3-8                    --\n",
      "|    └─Sequential: 2-17                  (recursive)\n",
      "|    |    └─Conv2d: 3-9                  (recursive)\n",
      "|    |    └─ReLU: 3-10                   --\n",
      "|    └─Sequential: 2-18                  (recursive)\n",
      "|    |    └─Conv2d: 3-11                 (recursive)\n",
      "|    |    └─ReLU: 3-12                   --\n",
      "|    |    └─MaxPool2d: 3-13              --\n",
      "├─Sequential: 1-7                        --\n",
      "|    └─Flatten: 2-19                     --\n",
      "|    └─Dropout: 2-20                     --\n",
      "|    └─Linear: 2-21                      37,752,832\n",
      "|    └─ReLU: 2-22                        --\n",
      "|    └─Dropout: 2-23                     --\n",
      "|    └─Linear: 2-24                      16,781,312\n",
      "|    └─ReLU: 2-25                        --\n",
      "|    └─Linear: 2-26                      40,970\n",
      "=================================================================\n",
      "Total params: 58,322,314\n",
      "Trainable params: 58,322,314\n",
      "Non-trainable params: 0\n",
      "=================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "=================================================================\n",
       "Layer (type:depth-idx)                   Param #\n",
       "=================================================================\n",
       "├─Sequential: 1-1                        --\n",
       "|    └─Conv2d: 2-1                       34,944\n",
       "|    └─ReLU: 2-2                         --\n",
       "|    └─MaxPool2d: 2-3                    --\n",
       "├─Sequential: 1-2                        --\n",
       "|    └─Conv2d: 2-4                       614,656\n",
       "|    └─ReLU: 2-5                         --\n",
       "|    └─MaxPool2d: 2-6                    --\n",
       "├─Sequential: 1-3                        --\n",
       "|    └─Conv2d: 2-7                       885,120\n",
       "|    └─ReLU: 2-8                         --\n",
       "├─Sequential: 1-4                        --\n",
       "|    └─Conv2d: 2-9                       1,327,488\n",
       "|    └─ReLU: 2-10                        --\n",
       "├─Sequential: 1-5                        --\n",
       "|    └─Conv2d: 2-11                      884,992\n",
       "|    └─ReLU: 2-12                        --\n",
       "|    └─MaxPool2d: 2-13                   --\n",
       "├─Sequential: 1-6                        --\n",
       "|    └─Sequential: 2-14                  (recursive)\n",
       "|    |    └─Conv2d: 3-1                  (recursive)\n",
       "|    |    └─ReLU: 3-2                    --\n",
       "|    |    └─MaxPool2d: 3-3               --\n",
       "|    └─Sequential: 2-15                  (recursive)\n",
       "|    |    └─Conv2d: 3-4                  (recursive)\n",
       "|    |    └─ReLU: 3-5                    --\n",
       "|    |    └─MaxPool2d: 3-6               --\n",
       "|    └─Sequential: 2-16                  (recursive)\n",
       "|    |    └─Conv2d: 3-7                  (recursive)\n",
       "|    |    └─ReLU: 3-8                    --\n",
       "|    └─Sequential: 2-17                  (recursive)\n",
       "|    |    └─Conv2d: 3-9                  (recursive)\n",
       "|    |    └─ReLU: 3-10                   --\n",
       "|    └─Sequential: 2-18                  (recursive)\n",
       "|    |    └─Conv2d: 3-11                 (recursive)\n",
       "|    |    └─ReLU: 3-12                   --\n",
       "|    |    └─MaxPool2d: 3-13              --\n",
       "├─Sequential: 1-7                        --\n",
       "|    └─Flatten: 2-19                     --\n",
       "|    └─Dropout: 2-20                     --\n",
       "|    └─Linear: 2-21                      37,752,832\n",
       "|    └─ReLU: 2-22                        --\n",
       "|    └─Dropout: 2-23                     --\n",
       "|    └─Linear: 2-24                      16,781,312\n",
       "|    └─ReLU: 2-25                        --\n",
       "|    └─Linear: 2-26                      40,970\n",
       "=================================================================\n",
       "Total params: 58,322,314\n",
       "Trainable params: 58,322,314\n",
       "Non-trainable params: 0\n",
       "================================================================="
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "\n",
    "summary(AlexNet(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "175e1991",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1/50]\t train_loss=0.0116, test_loss=0.0105, train_acc=0.1535 test_acc=0.2654\n",
      "[ 2/50]\t train_loss=0.0091, test_loss=0.0081, train_acc=0.3602 test_acc=0.4441\n",
      "[ 3/50]\t train_loss=0.0079, test_loss=0.0073, train_acc=0.4492 test_acc=0.4999\n",
      "[ 4/50]\t train_loss=0.0072, test_loss=0.0067, train_acc=0.5013 test_acc=0.5391\n",
      "[ 5/50]\t train_loss=0.0067, test_loss=0.0063, train_acc=0.5360 test_acc=0.5699\n",
      "[ 6/50]\t train_loss=0.0062, test_loss=0.0060, train_acc=0.5713 test_acc=0.5991\n",
      "[ 7/50]\t train_loss=0.0059, test_loss=0.0054, train_acc=0.5969 test_acc=0.6370\n",
      "[ 8/50]\t train_loss=0.0056, test_loss=0.0053, train_acc=0.6193 test_acc=0.6493\n",
      "[ 9/50]\t train_loss=0.0053, test_loss=0.0051, train_acc=0.6374 test_acc=0.6656\n",
      "[10/50]\t train_loss=0.0050, test_loss=0.0050, train_acc=0.6577 test_acc=0.6712\n",
      "[11/50]\t train_loss=0.0048, test_loss=0.0049, train_acc=0.6683 test_acc=0.6784\n",
      "[12/50]\t train_loss=0.0046, test_loss=0.0049, train_acc=0.6832 test_acc=0.6750\n",
      "[13/50]\t train_loss=0.0044, test_loss=0.0046, train_acc=0.6976 test_acc=0.6987\n",
      "[14/50]\t train_loss=0.0043, test_loss=0.0046, train_acc=0.7052 test_acc=0.6973\n",
      "[15/50]\t train_loss=0.0041, test_loss=0.0045, train_acc=0.7185 test_acc=0.7091\n",
      "[16/50]\t train_loss=0.0040, test_loss=0.0045, train_acc=0.7256 test_acc=0.7002\n",
      "[17/50]\t train_loss=0.0039, test_loss=0.0043, train_acc=0.7343 test_acc=0.7200\n",
      "[18/50]\t train_loss=0.0037, test_loss=0.0043, train_acc=0.7432 test_acc=0.7188\n",
      "[19/50]\t train_loss=0.0036, test_loss=0.0044, train_acc=0.7519 test_acc=0.7098\n",
      "[20/50]\t train_loss=0.0035, test_loss=0.0041, train_acc=0.7580 test_acc=0.7318\n",
      "[21/50]\t train_loss=0.0034, test_loss=0.0041, train_acc=0.7658 test_acc=0.7279\n",
      "[22/50]\t train_loss=0.0034, test_loss=0.0044, train_acc=0.7657 test_acc=0.7088\n",
      "[23/50]\t train_loss=0.0032, test_loss=0.0041, train_acc=0.7780 test_acc=0.7357\n",
      "[24/50]\t train_loss=0.0032, test_loss=0.0041, train_acc=0.7847 test_acc=0.7286\n",
      "[25/50]\t train_loss=0.0031, test_loss=0.0041, train_acc=0.7865 test_acc=0.7358\n",
      "[26/50]\t train_loss=0.0031, test_loss=0.0041, train_acc=0.7911 test_acc=0.7334\n",
      "[27/50]\t train_loss=0.0029, test_loss=0.0041, train_acc=0.8010 test_acc=0.7359\n",
      "[28/50]\t train_loss=0.0029, test_loss=0.0041, train_acc=0.8048 test_acc=0.7324\n",
      "[29/50]\t train_loss=0.0028, test_loss=0.0041, train_acc=0.8096 test_acc=0.7344\n",
      "[30/50]\t train_loss=0.0027, test_loss=0.0041, train_acc=0.8131 test_acc=0.7358\n",
      "[31/50]\t train_loss=0.0026, test_loss=0.0042, train_acc=0.8184 test_acc=0.7277\n",
      "[32/50]\t train_loss=0.0026, test_loss=0.0041, train_acc=0.8210 test_acc=0.7370\n",
      "[33/50]\t train_loss=0.0025, test_loss=0.0042, train_acc=0.8273 test_acc=0.7344\n",
      "[34/50]\t train_loss=0.0025, test_loss=0.0043, train_acc=0.8299 test_acc=0.7289\n",
      "[35/50]\t train_loss=0.0024, test_loss=0.0041, train_acc=0.8350 test_acc=0.7424\n",
      "[36/50]\t train_loss=0.0024, test_loss=0.0042, train_acc=0.8403 test_acc=0.7422\n",
      "[37/50]\t train_loss=0.0023, test_loss=0.0043, train_acc=0.8438 test_acc=0.7310\n",
      "[38/50]\t train_loss=0.0023, test_loss=0.0042, train_acc=0.8458 test_acc=0.7397\n",
      "[39/50]\t train_loss=0.0022, test_loss=0.0042, train_acc=0.8485 test_acc=0.7387\n",
      "[40/50]\t train_loss=0.0022, test_loss=0.0042, train_acc=0.8522 test_acc=0.7414\n",
      "[41/50]\t train_loss=0.0021, test_loss=0.0043, train_acc=0.8559 test_acc=0.7308\n",
      "[42/50]\t train_loss=0.0021, test_loss=0.0043, train_acc=0.8566 test_acc=0.7364\n",
      "[43/50]\t train_loss=0.0021, test_loss=0.0042, train_acc=0.8588 test_acc=0.7364\n",
      "[44/50]\t train_loss=0.0020, test_loss=0.0042, train_acc=0.8638 test_acc=0.7404\n",
      "[45/50]\t train_loss=0.0020, test_loss=0.0043, train_acc=0.8682 test_acc=0.7358\n",
      "[46/50]\t train_loss=0.0019, test_loss=0.0045, train_acc=0.8709 test_acc=0.7261\n",
      "[47/50]\t train_loss=0.0019, test_loss=0.0044, train_acc=0.8716 test_acc=0.7310\n",
      "[48/50]\t train_loss=0.0019, test_loss=0.0043, train_acc=0.8744 test_acc=0.7345\n",
      "[49/50]\t train_loss=0.0018, test_loss=0.0043, train_acc=0.8781 test_acc=0.7409\n",
      "[50/50]\t train_loss=0.0018, test_loss=0.0044, train_acc=0.8791 test_acc=0.7349\n"
     ]
    }
   ],
   "source": [
    "net = AlexNet(num_class).to(device)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "\n",
    "train_loss, test_loss, train_acc, test_acc = [], [], [], []\n",
    "\n",
    "for epoch in range(num_epoch):\n",
    "    net.train()\n",
    "    train_loss_epoch, train_acc_epoch, img_cnt = 0, 0, 0\n",
    "    for x, y in train_iter:\n",
    "        x, y = x.to(device), y.to(device)\n",
    "        pred: Tensor = net(x)\n",
    "        los = loss_func(pred, y)\n",
    "        los.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        train_loss_epoch += los.item()\n",
    "        img_cnt += y.shape[0]\n",
    "        train_acc_epoch += (pred.argmax(dim=1) == y).sum().item()\n",
    "    train_loss_epoch /= img_cnt\n",
    "    train_acc_epoch /= img_cnt\n",
    "    train_loss.append(train_loss_epoch)\n",
    "    train_acc.append(train_acc_epoch)\n",
    "    # ------\n",
    "    net.eval()\n",
    "    test_loss_epoch, img_cnt, test_acc_epoch = 0, 0, 0\n",
    "    with torch.no_grad():\n",
    "        for x, y in test_iter:\n",
    "            x, y = x.to(device), y.to(device)\n",
    "            pred: Tensor = net(x)\n",
    "            los = loss_func(pred, y)\n",
    "            test_loss_epoch += los.item()\n",
    "            test_acc_epoch += (pred.argmax(dim=1) == y).sum().item()\n",
    "            img_cnt += y.shape[0]\n",
    "\n",
    "    test_loss_epoch /= img_cnt\n",
    "    test_acc_epoch /= img_cnt\n",
    "\n",
    "    test_loss.append(test_loss_epoch)\n",
    "    test_acc.append(test_acc_epoch)\n",
    "\n",
    "    print(\n",
    "        f\"[{epoch+1:2d}/{num_epoch}]\\t train_loss={train_loss_epoch:.4f}, test_loss={test_loss_epoch:.4f}, train_acc={train_acc_epoch:.4f} test_acc={test_acc_epoch:.4f}\"\n",
    "    )\n",
    "    torch.save(net.state_dict(), f\"alex_cifar10_{epoch+1:03d}.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f2b750d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jcheng\\AppData\\Local\\Temp\\ipykernel_14952\\3188439808.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  net.load_state_dict(torch.load(\"alex_cifar10_050.pth\"))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = AlexNet(num_class).to(device)\n",
    "net.load_state_dict(torch.load(\"alex_cifar10_050.pth\"))\n",
    "net.eval()\n",
    "vis_data = tv.datasets.CIFAR10(\"./cifar10_dataset\", train=False, transform=transform)\n",
    "vis_iter = torch.utils.data.DataLoader(vis_data, batch_size=1, shuffle=True)\n",
    "\n",
    "_, ax = plt.subplots(1, 3)\n",
    "for i, (x, y) in enumerate(vis_iter):\n",
    "    if i >= 3:\n",
    "        break\n",
    "    x: Tensor = x.to(device)\n",
    "    y: Tensor = y.to(device)\n",
    "    pred: Tensor = net(x).argmax()\n",
    "    x.squeeze_()\n",
    "    y.squeeze_()\n",
    "    pred.squeeze_()\n",
    "    ax[i].set_title(f\"{y.data}, pred={pred.data}\")\n",
    "    ax[i].imshow(x.cpu().permute(1, 2, 0).numpy())\n",
    "    ax[i].axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
